Author(s)

Andreas Karathanasopoulos^1*, Adam Zaremba^1,2, Mohammed Osman¹, Mateusz Mikutowski²

¹Dubai Business School, University of Dubai, Dubai, UAE.
²Poznan University of Economics and Business, Poznan, Poland.

The motivation for this research paper is the application of two novel models in the prediction of crude oil index. The first model is a generic deep belief network and the second model is an adaptive neural fuzzy inference system. Furthermore we have to emphasize on the second contribution in this paper which is the use of an extensive number of inputs including mixed and autoregressive inputs. Both proposed methodologies have been used in the past in different problems such as face recognition, prediction of chromosome anomalies etch, providing higher outputs than usual. For comparison purposes, the forecasting statistical and empirical accuracy of models is benchmarked with traditional strategies such as a naive strategy, a moving average convergence divergence model and an autoregressive moving average model. As it turns out, the proposed novel techniques produce higher statistical and empirical results outperforming the other linear models. Concluding first time such research work brings such outstanding outputs in terms of forecasting oil markets.

Future Contract, Crude Oil, Deep Beliefs, ANFIS Model

Karathanasopoulos, A. , Zaremba, A. , Osman, M. and Mikutowski, M. (2019) Oil Forecasting Using Artificial Intelligence. Theoretical Economics Letters, 9, 2283-2290. doi: 10.4236/tel.2019.97144.